In a typical cellular radio system, wireless terminals (also known as mobile stations and/or user equipment units (UEs)) communicate via a radio access network (RAN) to one or more core networks. The radio access network (RAN) covers a geographical area which is divided into cell areas, with each cell area being served by a base station, e.g., a radio base station (RBS), which in some networks may also be called, for example, a “NodeB” (UMTS) or “eNodeB” (LTE). A cell is a geographical area where radio coverage is provided by the radio base station equipment at a base station site. Each cell is identified by an identity within the local radio area, which is broadcast in the cell. The base stations communicate over the air interface operating on radio frequencies with the user equipment units (UE) within range of the base stations.
In some versions of the radio access network, several base stations are typically connected (e.g., by landlines or microwave) to a controller node (such as a radio network controller (RNC) or a base station controller (BSC)) which supervises and coordinates various activities of the plural base stations connected thereto. The radio network controllers are typically connected to one or more core networks.
The Universal Mobile Telecommunications System (UMTS) is a third generation mobile communication system, which evolved from the second generation (2G) Global System for Mobile Communications (GSM). UTRAN is essentially a radio access network using wideband code division multiple access for user equipment units (UEs). In a forum known as the Third Generation Partnership Project (3GPP), telecommunications suppliers propose and agree upon standards for third generation networks and UTRAN specifically, and investigate enhanced data rate and radio capacity. Specifications for the Evolved Universal Terrestrial Radio Access Network (E-UTRAN) are ongoing within the 3rd Generation Partnership Project (3GPP). The Evolved Universal Terrestrial Radio Access Network (E-UTRAN) comprises the Long Term Evolution (LTE) and System Architecture Evolution (SAE). Long Term Evolution (LTE) is a variant of a 3GPP radio access technology wherein the radio base station nodes are connected to a core network (via Access Gateways, or AGWs) rather than to radio network controller (RNC) nodes. In general, in LTE the functions of a radio network controller (RNC) node are distributed between the radio base stations nodes (eNodeB's in LTE) and AGWs. As such, the radio access network (RAN) of an LTE system has an essentially “flat” architecture comprising radio base station nodes without reporting to radio network controller (RNC) nodes.
Long Term Evolution (LTE) uses single-carrier frequency-division multiple access (SC-FDMA) in an uplink direction from the wireless terminal to the eNodeB. SC-FDMA is advantageous in terms of power amplifier (PA) efficiency since, e.g., the SC-FDMA signal has a smaller peak-to-average ratio than an orthogonal frequency division multiple access (OFDM) signal. However, SC-FDMA gives rise to inter-symbol interference (ISI) problem in dispersive channels. Addressing inter-symbol interference (ISI) can enable SC-FDMA to improve power amplifier efficiency without sacrificing performance.
Frequency-domain (FD) linear equalization (LE) is commonly used in the LTE uplink to deal with inter-symbol interference (ISI). In frequency domain linear equalization, inter-symbol interference (ISI) is modeled as colored noise, which is then suppressed by the linear equalization. A popular linear equalization approach is linear minimum mean square error (LMMSE) equalization. Linear minimum mean square error (LMMSE) equalization is described, e.g., by H. Sari, G. Karam, and I. Jeanclaude, “Frequency-domain equalization of mobile radio and terrestrial broadcast channels,” in Proc. IEEE Global Telecommun. Conf., vol. 1, November 1994, which is incorporated herein by reference in its entirety. However, performance of LMMSE equalization is limited. When the allocated bandwidth is large and when the channel is highly dispersive, a more sophisticated receiver is needed in order to ensure robust reception.
Soft cancellation-based MMSE turbo equalization has been considered for use on the uplink in LTE. With a receiver using soft cancellation-based MMSE turbo equalization, inter-symbol interference (ISI) is cancelled via soft decision-feedback equalization (DFE), where the tentatively detected soft symbols are determined based on turbo decoder outputs. The performance of such a receiver improves when more information exchanges between the decoder and soft DFE/demodulator take place. Although turbo equalization achieves superior performance, it incurs a large latency due to the iterative demodulation and decoding process.
Maximum-likelihood detection (MLD) is a well-known approach to address the inter-symbol interference (ISI) and multiple input/multiple output (MIMO) interference. Maximum-likelihood detection (MLD) does not involve the decoder cooperation and thus does not incur as a long latency as turbo equalization does. However, when there are too many overlapping symbols, Maximum-likelihood detection (MLD) becomes impractical due to complexity.
Codes with a tree structure have been used in the equalization of band-limited nonlinear channels by sequence estimation. Since it is generally not practical to view and weigh all the branches in a tree structured code, a search algorithm is usually employed. Code searching algorithms may be classified in various ways, such as sorting or non-sorting, depth-first, breadth-first, or metric-first (where the metric is some measure of likelihood). A purely breadth-first algorithm that sorts is the M-algorithm. The M-algorithm is described, e.g., in the following: Choi et al., “Efficient Soft-Input Soft-Output MIMO Detection Via Improved M-Algorithm”, Proceedings of 2010 IEEE International Conference on Communications; Baek et al., “Combined QRD-M and DFE Detection Technique for Simple and Efficient Signal Detection in MIMO-OFDM Systems”, IEEE Transactions on Wireless Communications, Vol. 8, No. 4, April 2009; pages 1632-1638; Jelinek et al., “Instrumental Tree Encoding of Information Sources”, IEEE Transactions on Information Theory, January 1971, pp. 118-119; and Anderson et al., “Sequential Coding Algorithms: A Survey and Cost Analysis”, IEEE Transactions on Communications, Vol. COM-32, No. 2, February 1984, pages 169-176, all of which are incorporated herein by reference.